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replied to singhsidhukuldeep's post 13 days ago
Fascinating new research alert! Just read a groundbreaking paper on understanding Retrieval-Augmented Generation (RAG) systems and their performance factors. Key insights from this comprehensive study: >> Architecture Deep Dive The researchers analyzed RAG systems across 6 datasets (3 code-related, 3 QA-focused) using multiple LLMs. Their investigation revealed critical insights into four key design factors: Document Types Impact: • Oracle documents (ground truth) aren't always optimal • Distracting documents significantly degrade performance • Surprisingly, irrelevant documents boost code generation by up to 15.6% Retrieval Precision: • Performance varies dramatically by task • QA tasks need 20-100% retrieval recall • Perfect retrieval still fails up to 12% of the time on previously correct instances Document Selection: • More documents ≠ better results • Adding documents can cause errors on previously correct samples • Performance degradation increases ~1% per 5 additional documents in code tasks Prompt Engineering: • Most advanced prompting techniques underperform simple zero-shot prompts • Technique effectiveness varies significantly across models and tasks • Complex prompts excel at difficult problems but struggle with simple ones >> Technical Implementation The study utilized: • Multiple retrievers including BM25, dense retrievers, and specialized models • Comprehensive corpus of 70,956 unique API documents • Over 200,000 API calls and 1,000+ GPU hours of computation • Sophisticated evaluation metrics tracking both correctness and system confidence 💡 Key takeaway: RAG system optimization requires careful balancing of multiple factors - there's no one-size-fits-all solution.
replied to julien-c's post 14 days ago
After some heated discussion 🔥, we clarify our intent re. storage limits on the Hub TL;DR: - public storage is free, and (unless blatant abuse) unlimited. We do ask that you consider upgrading to PRO and/or Enterprise Hub if possible - private storage is paid above a significant free tier (1TB if you have a paid account, 100GB otherwise) docs: https://huggingface.co./docs/hub/storage-limits We optimize our infrastructure continuously to scale our storage for the coming years of growth in Machine learning, to the benefit of the community 🔥 cc: @reach-vb @pierric @victor and the HF team
new activity about 1 month ago
syvai/hviske-v2:Tegnsætning og store bogstaver
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